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Multi-agent reinforcement learning also suffers from the credit assignment problem: when the team receives a reward, it is difficult to determine which agent contributed the most.
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11/1/2026
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UnverifiedThe FOCUS method uses selective branching and active learning to alleviate two problems in credit assignment for long-horizon agents under sparse outcome rewards: inaccurate critic value estimates and the high cost of continuation sampling.75% similarUnverifiedCredit assignment is a core challenge in reinforcement learning: when a trajectory receives only a single scalar return, it is difficult to judge the contribution of each step's action; in a 1000-token chain of thought with only a final-answer signal, the intermediate steps have no direct feedback, resulting in extremely high policy gradient variance.74% similarUnverifiedThe credit assignment problem refers to the fact that in multi-step agent tasks, traditional rewards are only given as a total score upon final task completion, providing no information about which step was right or wrong, and it is one of the most central engineering challenges in agentic RL research.70% similarVerifiedSparse rewards are one of the most challenging problems in reinforcement learning, where an agent only receives a reward upon completing a specific objective.70% similarUnverifiedUnbiased credit assignment alone is insufficient to guarantee reliable learning; sufficiently frequent and informative feedback is equally necessary.69% similar
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